A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) fault-tolerant control of a six-phase permanent magnet synchronous motor (PMSM) position servo drive is proposed in this study. First, the fault detection and operating decision method of the six-phase PMSM position servo drive is developed. Then, an ideal computed torque controller is designed for the tracking of the rotor position reference command. In general, it is impossible to design an ideal computed control law owing to the uncertainties of the six-phase PMSM position servo drive, which are difficult to know in advance for practical applications. Therefore, the RFNCMAN, which combined the merits of a recurrent fuzzy cerebellar model articulation network (RFCMAN) and a recurrent fuzzy neural network (RFNN), is proposed to estimate a nonlinear equation included in the ideal computed control law with a robust compensator designed to compensate the minimum reconstructed error. Furthermore, the adaptive learning algorithm for the online training of the RFNCMAN is derived using the Lyapunov stability to guarantee the closed-loop stability. Finally, the proposed RFNCMAN fault-tolerant control system is implemented in a 32-bit floating-point DSP. The effectiveness of the six-phase PMSM position servo drive using the proposed intelligent fault-tolerant control system is verified by some experimental results. Index Terms-Recurrent fuzzy neural cerebellar model articulation network (RFNCMAN), fault-tolerant control, six-phase permanent magnet synchronous motor (PMSM), Lyapunov stability, Taylor series expansion.

A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) control is proposed in this paper for position servo drive systems to track various periodical position references with robustness. The adopted position servo drive system is designed using a six-phase PMSM and equipped with a fault-tolerant control scheme. First, an ideal computed torque controller is designed for the tracking of the rotor position reference command. Since the uncertainties of the PMSM position servo drive system are difficult to know in advance, it is impossible to design an ideal computed control law for practical applications. Therefore, the RFNCMAN is proposed to mimic the ideal computed torque controller with a compensated controller to compensate the approximation error. In the RFNCMAN, a recurrent fuzzy cerebellar model articulation network (RFCMAN) is adopted in the first dimension to enhance the online learning rate and localisation learning capability. Moreover, a general recurrent fuzzy neural network (RFNN) is adopted in the second dimension to enhance the generalisation performance and to reduce the required memory and rule numbers. Finally, the proposed position control system is implemented in a 32-bit floating-point DSP. The effectiveness of the proposed RFNCMAN control system is verified by some experimental results.

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By imitating the vibration isolation mechanism and special organic texture of woodpecker’s brain, a bionics mechanics-based structure model is constructed for the vibration isolation system of ultra-precision device. In consideration of compound vibration environment and non-linearity of the ultra-precision device, a fuzzy-PID (PID stands for Proportional, Integral and Derivative.) active vibration control system is developed, its operation is that fuzzy control is adopted while the error is big and PID control is adopted while the error is small. The performance of the vibration control system is validated by numerical simulation. The simulation results show that the bionic vibration control system has good performance against floor disturbances and direct disturbing force. It can be applied to the vibration isolation system of the ultra-precision measuring and manufacturing device.

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